Hands-on Tutorial Research Articles

Calculating risk and prevalence ratios and differences in R: Developing intuition with a hands-on tutorial and code

Calculating risk and prevalence ratios and differences in R: Developing intuition with a hands-on tutorial and code

Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project.

Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym's synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.

Gas-lifted systems: A hands-on tutorial on modelling and analysis

Gas lift is deplored when the natural lift energy for matured well provides flow rate below the required value. Casing-heading instability removal, fault-tolerant control (FTC) and general operation of the system require the system model and its analysis. In this article, we present a simple approach to gas lift modelling from a tutorial perspective. We derive these gas-lifted system models comparing the models with existing models in the process. We also derive the pressure profile in the annulus and tubing. A simplified approach to the linearisation of the gas-lifted system that is a differential algebraic equation (DAE) system is also presented. The linearised result is used in simulation and analysis of the gas-lifted system where the phase portrait and the stability of the linearised system were considered. Also considered were behaviour of the linearised system and the nonlinear counterpart. The difference of 4kg, 4kg and 30kg corresponding to percentage difference of 0.16%, 0.29% and 2.38% respectively between the linearised and the nonlinear states observed implies convergence. The key objective of this work is to present a simplified approach to the modelling and analysis of the gas-lifted system.

Calculating risk and prevalence ratios and differences in R: developing intuition with a hands-on tutorial and code

Epidemiologic research questions often focus on evaluating binary outcomes, yet curricula and scientific literature do not always provide clear guidance or examples on selecting and calculating an appropriate measure of association in these scenarios. Reporting inappropriate measures may lead to misleading statistical conclusions. We present a hands-on tutorial that includes annotated code written in an open-source statistical programming language (R) showing readers how to apply, compare, and understand four methods used to estimate a risk or prevalence ratio (or difference), rather than presenting an odds ratio. We will provide guidance on when to use each method, discussing the strengths and limitations of each approach, and compare the results obtained across them. Ultimately, we aim to help trainees, public health researchers, and interdisciplinary professionals develop an intuition for these methods and empower them to implement and interpret these methods in their own research.

The Vine Street Irregulars: A Chronicle of Graduate Student Life and Politics at the University of Nebraska-Lincoln 1975–1976

The memories recalled in the twenty-seven essays in the volume are anchored in sometimes intense and sometimes admittedly naive graduate student experiences at the University of Nebraska-Lincoln during the 1970’s. Master’s degree in hand, I returned to graduate study at Nebraska in 1972 after four years in the military, benefitting not only from the G.I. Bill, but also from a National Defense Education Act Fellowship. I was increasingly wise to the ways of administrative bureaucrats who were sometimes enlightened, sometimes punitive, draconian, and exploitive, or — far more often — simply moribund. As adult students, we wanted fair play and respect. I pursued those goals at Nebraska as: (a) the Geography Department representative to the University Graduate Student Association, (b) as an early vicepresident of the Graduate Student Association, (c) as UNL’s graduate student voting member on the three-campus System Graduate Council, (d) as a voting member on two Vice-Chancellor Search Committees, and finally (e) as the UNL Graduate Student Representative investigating and voting on a fellow graduate student’s formal (and contentious) grade appeal. In these various roles, many students told me stories that made my toes curl. The cumulative result was a hands-on tutorial in bureaucratic/administrative machinations as they ground onward day after day on the Nebraska campuses. Subsequently, writing the essays that became The Vine Street Irregulars gave me a way to explore, in public, the issues, problems, and experiences that bedeviled the lives of far too many graduate students on the University of Nebraska campus during the mid 1970s. Now, nearly fifty years later, this volume preserves some of those struggles and hopefully captures parts of the socio-spatial milieu in which they unfolded. Minor errors and a few awkward phrases have been silently repaired. Where potentially useful, explanatory footnotes are appended.

A Hands-On Tutorial for Systematic Review and Meta-Analysis With Example Data Set and Codes.

Systematic review and meta-analysis are regarded as standard and valuable tools for providing an objective and reproducible synthesis of research findings in the literature. Their increasing popularity has led to heightened expectations for comprehensiveness and rigor in conducting scientific reviews and analyses. The purpose of this tutorial is to provide an overview and guidance on how to address the key elements for conducting a high-quality systematic review with meta-analysis. This tutorial describes practical guidelines to conduct systematic review and meta-analysis for researchers who are interested in learning and applying the method. Drawing on previous meta-analytic reviews of second-language learning as illustrative examples, it discusses the methodological choices and judgment calls in each step of the review and analysis process. As a hands-on tutorial, it uses a published data set concerning the role of talker variability in speech training studies as a running example to elucidate the statistical process and interpret the results of meta-analysis with freely available R software. This tutorial provides a walk-through of the methodological choices, controversial issues, and common practices in conducting systematic reviews and meta-analyses. The data set and R codes are offered as publicly accessible supplementary materials (https://osf.io/e9bkf/) for replication and practice, which we hope will motivate more applications in the speech, language, and hearing sciences field as well as behavioral and social sciences research in general.

The Franka Emika Robot: A Reference Platform for Robotics Research and Education

The importance of robots for industry, research, education, and society as a whole is steadily increasing as reflected by the number of available systems and installed robots, not only in industry but also in the public sector and households. Software-only robotics researchers usually rely on commercially available robots which, in the case of manipulators, are primarily designed for industrial purposes and are often far from their needs. This article is a hands-on tutorial on the Franka Emika robot, the first series of industrial artificial intelligence (AI)-ready tactile robot platforms. Beyond industrial use, the systems can be seamlessly expanded to fulfill the demands of research and education across all robotics and AI disciplines. To satisfy the needs of such a wide variety of fields, it provides three different interfaces: Desk, a high-level app-based user interface for easy and fast task programming; Robot Integrated Development Environment (RIDE), a command-based programming environment used to create high-performance robot skills that enables programming custom apps and integrating external sensors; and the Franka control interface (FCI), a 1-kHz low-level torque and position control interface that exploits the also-available Langrangian dynamics robot model. We take a close look at implementations with all interfaces, ranging from simple solutions, apps, and controllers to robot-learning examples illustrating how to exploit all the advantages of this platform in ongoing robotics research and education.

Continuing the Role of the Citizen Scientist

New introductions to invasive mosquitoes, Aedes aegypti and Aedes albopictus, increase the risk for vector-borne diseases such as dengue, chikungunya, and Zika in the United States. Tracking these new introductions is more important than ever. This lesson plan focuses on the collection of mosquito larvae and pupae before the onset of summer with a focus on insect development. Students will observe the immature mosquitoes grow and metamorphose into adults. Novel aspects include collecting larvae and pupae around the home/school, observing the mosquito life cycle by safely rearing them from immature stages (larvae and pupae) to adults, and learning important background information on mosquito biology and pathogens that mosquitoes can transmit. The lesson describes new tools to use with the Invasive Mosquito Project, an international citizen science–based mosquito surveillance program exploring mosquitoes and the pathogens they may transmit to interested community members (including students and teachers) and their companion animals. This project is a stand-alone or follow-up lesson plan to the mosquito egg collection lesson used prior to the onset of winter (see Cohnstaedt et al., 2016). Students are able to participate in a hands-on tutorial to build a sealed emergence chamber to safely raise mosquitoes. Long-term data sets can also be used by teachers and students for further classroom discussions on the risks of mosquito-borne illnesses nationwide. This lesson further focuses on how individuals must play an active role in protecting their communities and pets from illness and increasing awareness of the dangerous pathogens mosquitoes can transmit and the importance of mosquito management. Materials from this lesson plan (available at http://www.citizenscience.us) can be adapted for each classroom but are best-suited for middle school to high school classes, as well as Advanced Placement classes.

PowerLAPIM: An application to conduct power analysis for linear and quadratic longitudinal actor–partner interdependence models in intensive longitudinal dyadic designs

The longitudinal actor–partner interdependence model (L-APIM) is used to study actor and partner effects, both linear and curvilinear, in dyadic intensive longitudinal data. A burning question is how to conduct power analyses for different L-APIM variants. In this paper, we introduce an accessible power analysis application, called PowerLAPIM, and provide a hands-on tutorial for conducting simulation-based power analyses for 32 L-APIM variants. With PowerLAPIM, we target the number of dyads needed, but not the number of repeated measurements for both partners (which is often fixed in longitudinal studies). PowerLAPIM allows to study moderation of linear and quadratic actor and partner effects by incorporating time-varying covariates or a categorical dyad-level predictor to test group differences. We also provide the functionality to account for serial dependency in the outcome variable by including autoregressive effects. Building on existing study that can yield estimates and thus plausible values of relevant model parameters, we illustrate how to perform a power analysis for a future study. In this illustration, we also demonstrate how to run a sensitivity analysis, to assess the impact of uncertainty about the model parameters, and of changes in the number of repeated measurements.

FastISM: performant in silico saturation mutagenesis for convolutional neural networks.

Deep-learning models, such as convolutional neural networks, are able to accurately map biological sequences to associated functional readouts and properties by learning predictive de novo representations. In silico saturation mutagenesis (ISM) is a popular feature attribution technique for inferring contributions of all characters in an input sequence to the model's predicted output. The main drawback of ISM is its runtime, as it involves multiple forward propagations of all possible mutations of each character in the input sequence through the trained model to predict the effects on the output. We present fastISM, an algorithm that speeds up ISM by a factor of over 10× for commonly used convolutional neural network architectures. fastISM is based on the observations that the majority of computation in ISM is spent in convolutional layers, and a single mutation only disrupts a limited region of intermediate layers, rendering most computation redundant. fastISM reduces the gap between backpropagation-based feature attribution methods and ISM. It far surpasses the runtime of backpropagation-based methods on multi-output architectures, making it feasible to run ISM on a large number of sequences. An easy-to-use Keras/TensorFlow 2 implementation of fastISM is available at https://github.com/kundajelab/fastISM. fastISM can be installed using pip install fastism. A hands-on tutorial can be found at https://colab.research.google.com/github/kundajelab/fastISM/blob/master/notebooks/colab/DeepSEA.ipynb. Supplementary data are available at Bioinformatics online.

A hands-on tutorial on network and topological neuroscience

The brain is an extraordinarily complex system that facilitates the optimal integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pairwise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses the 1000 Functional Connectomes Project dataset. Moreover, we would like to highlight one part of our notebook dedicated to the realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pairwise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.

Natural Language Processing: Practical Applications in Medicine and Investigation of Contextual Autocomplete.

Natural language processing (NLP) is the task of converting unstructured human language data into structured data that a machine can understand. While its applications are far and wide in healthcare, and are growing considerably every day, this chapter will focus on one particularly relevant application for healthcare professionals-reducing the burden of clinical documentation. More specifically, the chapter will discuss two studies (Gopinath et al., Fast, structured clinical documentation via contextual autocomplete. arXiv: 2007.15153, 2020; Greenbaum et al., Contextual autocomplete: a novel user interface using machine learning to improve ontology usage and structured data capture for presenting problems in the emergency department, 2017) that have implemented contextual autocompletion in electronic medical records and their promising results with regards to time saved for clinicians. The goals of this chapter are to introduce to the curious healthcare provider the basics of natural language processing, zoom into the use case of contextual autocomplete for electronic medical records, and provide a hands-on tutorial that introduces the basic NLP concepts required to build a model for predictive suggestions.

Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables

BackgroundOne of the emerging themes in epidemiology is the use of interval estimates. Currently, three interval estimates for confidence (CI), prediction (PI), and tolerance (TI) are at a researcher's disposal and are accessible within the open access framework in R. These three types of statistical intervals serve different purposes. Confidence intervals are designed to describe a parameter with some uncertainty due to sampling errors. Prediction intervals aim to predict future observation(s), including some uncertainty present in the actual and future samples. Tolerance intervals are constructed to capture a specified proportion of a population with a defined confidence. It is well known that interval estimates support a greater knowledge gain than point estimates. Thus, a good understanding and the use of CI, PI, and TI underlie good statistical practice. While CIs are taught in introductory statistical classes, PIs and TIs are less familiar.ResultsIn this paper, we provide a concise tutorial on two-sided CI, PI and TI for binary variables. This hands-on tutorial is based on our teaching materials. It contains an overview of the meaning and applicability from both a classical and a Bayesian perspective. Based on a worked-out example from veterinary medicine, we provide guidance and code that can be directly applied in R.ConclusionsThis tutorial can be used by others for teaching, either in a class or for self-instruction of students and senior researchers.

FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, S&P 500, HSI, SSE 50, and CSI 300; (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link https://github.com/AI4Finance-LLC/FinRL-Library.

Quality of abdominal ultrasound image acquisition by novice practitioners following a minimal training session on healthy volunteers.

Point of care ultrasound (POCUS) is an essential tool for physicians to guide treatment decisions in both hospital and prehospital settings. Despite the potential patient care and system utilization benefits of prehospital ultrasound, the financial burden of a "hands-on" training program for large numbers of paramedics remains a barrier to implementation. In this study, we conducted a prospective, observational, double-blinded study comparing paramedics to emergency physicians in their ability to generate usable abdominal ultrasound images after a 1-hour didactic training session. Canadian aeromedical critical care paramedics were compared against emergency medicine physicians in their ability to generate adequate abdominal ultrasound images on five healthy volunteers. Quality of each scan was evaluated by a trained expert in POCUS who was blinded to the identity of the participant using a 5-point Likert scale and using the standardized QUICk Focused Assessment with Sonography in Trauma (FAST) assessment tool. Fourteen Critical care paramedics and four emergency department (ED) physicians were voluntarily recruited. Of paramedics, 57% had never used ultrasound before, 36% has used ultrasound without formal training, and 7% had previous training. Physicians had a higher proportion of usable scans compared with paramedics (100% v. 61.4%, Δ38.6%; 95% confidence interval, 19.3-50.28). Paramedics were not able to produce images of interpretable quality at the same frequency when compared with emergency medicine physicians. However, a 61.4% usable image rate for paramedics following a short 1-hour didactic training session is promising for future studies, which could incorporate a short hands-on tutorial while remaining cost-effective.

Overview of image-to-image translation by use of deep neural networks: Denoising, super-resolution, modality conversion, and reconstruction in medical imaging

Since the advent of deep convolutional neural networks (DNNs), computer vision has seen an extremely rapid progress that has led to huge advances in medical imaging. Every year, many new methods are reported at conferences such as the International Conference on Medical Image Computing and Computer-Assisted Intervention and Machine Learning for Medical Image Reconstruction, or published online at the preprint server arXiv. There is a plethora of surveys on applications of neural networks in medical imaging (see [1] for a relatively recent comprehensive survey). This article does not aim to cover all aspects of the field, but focuses on a particular topic, image-to-image translation. Although the topic may not sound familiar, it turns out that many seemingly irrelevant applications can be understood as instances of image-to-image translation. Such applications include (1) noise reduction, (2) super-resolution, (3) image synthesis, and (4) reconstruction. The same underlying principles and algorithms work for various tasks. Our aim is to introduce some of the key ideas on this topic from a uniform viewpoint. We introduce core ideas and jargon that are specific to image processing by use of DNNs. Having an intuitive grasp of the core ideas of applications of neural networks in medical imaging and a knowledge of technical terms would be of great help to the reader for understanding the existing and future applications. Most of the recent applications which build on image-to-image translation are based on one of two fundamental architectures, called pix2pix and CycleGAN, depending on whether the available training data are paired or unpaired (see Sect. 1.3). We provide codes ([2, 3]) which implement these two architectures with various enhancements. Our codes are available online with use of the very permissive MIT license. We provide a hands-on tutorial for training a model for denoising based on our codes (see Sect. 6). We hope that this article, together with the codes, will provide both an overview and the details of the key algorithms and that it will serve as a basis for the development of new applications.

FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, SP (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link this https URL.

Boosting Functional Regression Models with FDboost

The R add-on package FDboost is a flexible toolbox for the estimation of functional regression models by model-based boosting. It provides the possibility to fit regression models for scalar and functional response with effects of scalar as well as functional covariates, i.e., scalar-on-function, function-on-scalar and function-on-function regression models. In addition to mean regression, quantile regression models as well as generalized additive models for location scale and shape can be fitted with FDboost. Furthermore, boosting can be used in high-dimensional data settings with more covariates than observations. We provide a hands-on tutorial on model fitting and tuning, including the visualization of results. The methods for scalar-on-function regression are illustrated with spectrometric data of fossil fuels and those for functional response regression with a data set including bioelectrical signals for emotional episodes.

SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting.

High-dimensional variable selection in the proportional hazards (PH) model has many successful applications in different areas. In practice, data may involve confounding variables that do not satisfy the PH assumption, in which case the stratified proportional hazards (SPH) model can be adopted to control the confounding effects by stratification without directly modeling the confounding effects. However, there is a lack of computationally efficient statistical software for high-dimensional variable selection in the SPH model. In this work an R package, SurvBoost, is developed to implement the gradient boosting algorithm for fitting the SPH model with high-dimensional covariate variables. Simulation studies demonstrate that in many scenarios SurvBoost can achieve better selection accuracy and reduce computational time substantially compared to the existing R package that implements boosting algorithms without stratification. The proposed R package is also illustrated by an analysis of gene expression data with survival outcome in The Cancer Genome Atlas study. In addition, a detailed hands-on tutorial for SurvBoost is provided.

Quantitative Systems Pharmacology and Physiologically-Based Pharmacokinetic Modeling With mrgsolve: A Hands-On Tutorial.

mrgsolve is an open‐source R package available on the Comprehensive R Archive Network. It combines R and C++ coding for simulation from hierarchical, ordinary differential equation–based models. Its efficient simulation engine and integration into a parallelizable, R‐based workflow makes mrgsolve a convenient tool both for simple and complex models and thus is ideal for physiologically‐based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) model. This tutorial will first introduce the basics of the mrgsolve simulation workflow, including model specification, the introduction of interventions (dosing events) into the simulation, and simulated results postprocessing. An applied simulation example is then presented using a PBPK model for voriconazole, including a model validation step against adult and pediatric data sets. A final simulation example is then presented using a previously published QSP model for mitogen‐activated protein kinase signaling in colorectal cancer, illustrating population simulation of different combination therapies.